Agilent CGH data
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@jhs1jjmleedsacuk-2338
Last seen 8.4 years ago
R 2.5.0 on openSUSE 10.2 x86_64. Hi, I'm using the arrayQuality package to analyse 3 44k Agilent CGH arrays with the aim of identifying regions of gain/loss. With the HTML report generated using the agQuality function i'm not getting the coloured loess curve on the MA plot for raw M. Additionally i'm only getting 1 value for the dot plot of controls normalized M values (-)3xLv1 (n=330) and likewise for the control A values. Alternatively when I run the maQualityPlots function on my mraw object created in marray I get these but don't get the comparative box plot. Firstly is this important as I'm unsure of how useful the comparative boxplots are as some values are NA? Secondly is this an appropriate tool to use and are there any others that may be of more use both for quality control and for analysis further down the line? Thankyou kindly for any input. Regards John
CGH arrayQuality marray CGH arrayQuality marray • 927 views
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@sean-davis-490
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jhs1jjm at leeds.ac.uk wrote: > R 2.5.0 on openSUSE 10.2 x86_64. > > Hi, > > I'm using the arrayQuality package to analyse 3 44k Agilent CGH arrays with the > aim of identifying regions of gain/loss. > > With the HTML report generated using the agQuality function i'm not getting the > coloured loess curve on the MA plot for raw M. Additionally i'm only getting 1 > value for the dot plot of controls normalized M values (-)3xLv1 (n=330) and > likewise for the control A values. Alternatively when I run the maQualityPlots > function on my mraw object created in marray I get these but don't get the > comparative box plot. > > Firstly is this important as I'm unsure of how useful the comparative boxplots > are as some values are NA? Secondly is this an appropriate tool to use and are > there any others that may be of more use both for quality control and for > analysis further down the line? Thankyou kindly for any input. Hi, John. Are these CGH arrays or expression arrays? The two probably need some different treatment. You imply you are using CGH arrays in looking for regions of gain/loss. Is this the case? Sean
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Sean Davis wrote: > jhs1jjm at leeds.ac.uk wrote: >> R 2.5.0 on openSUSE 10.2 x86_64. >> >> Hi, >> >> I'm using the arrayQuality package to analyse 3 44k Agilent CGH arrays with the >> aim of identifying regions of gain/loss. >> >> With the HTML report generated using the agQuality function i'm not getting the >> coloured loess curve on the MA plot for raw M. Additionally i'm only getting 1 >> value for the dot plot of controls normalized M values (-)3xLv1 (n=330) and >> likewise for the control A values. Alternatively when I run the maQualityPlots >> function on my mraw object created in marray I get these but don't get the >> comparative box plot. >> >> Firstly is this important as I'm unsure of how useful the comparative boxplots >> are as some values are NA? Secondly is this an appropriate tool to use and are >> there any others that may be of more use both for quality control and for >> analysis further down the line? Thankyou kindly for any input. > > Hi, John. Are these CGH arrays or expression arrays? The two probably > need some different treatment. You imply you are using CGH arrays in > looking for regions of gain/loss. Is this the case? And, then, of course, there is the subject, "Agilent CGH data"--SORRY! In this case, you do not want to rely on loess or other non-linear normalization methods. Also, the MA plots for the best arrays DO show a positive slope--this is totally expected and sought after. In other words, with higher M-values, we expect higher A-values. We have found that a pretty good measure of quality of CGH arrays is the dlrs: dlrs <- function(x) { nx <- length(x) if (nx<3) { stop("Vector length>2 needed for computation") } tmp <- embed(x,2) diffs <- tmp[,2]-tmp[,1] dlrs <- IQR(diffs)/(sqrt(2)*1.34) return(dlrs) } Run this on the Log ratios (ordered by chromosome and position). Good values are less than 0.2 or so, but even some slightly higher can be used. As for analysis, you may want to look into the snapCGH package, as it allows multiple analyses to be run with the same data structures. Sean
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Quoting Sean Davis <sdavis2 at="" mail.nih.gov=""> on Tue 25 Sep 2007 17:50:10 BST: > Sean Davis wrote: > > jhs1jjm at leeds.ac.uk wrote: > >> R 2.5.0 on openSUSE 10.2 x86_64. > >> > >> Hi, > >> > >> I'm using the arrayQuality package to analyse 3 44k Agilent CGH arrays > with the > >> aim of identifying regions of gain/loss. > >> > >> With the HTML report generated using the agQuality function i'm not > getting the > >> coloured loess curve on the MA plot for raw M. Additionally i'm only > getting 1 > >> value for the dot plot of controls normalized M values (-)3xLv1 (n=330) > and > >> likewise for the control A values. Alternatively when I run the > maQualityPlots > >> function on my mraw object created in marray I get these but don't get > the > >> comparative box plot. > >> > >> Firstly is this important as I'm unsure of how useful the comparative > boxplots > >> are as some values are NA? Secondly is this an appropriate tool to use and > are > >> there any others that may be of more use both for quality control and for > >> analysis further down the line? Thankyou kindly for any input. > > > > Hi, John. Are these CGH arrays or expression arrays? The two probably > > need some different treatment. You imply you are using CGH arrays in > > looking for regions of gain/loss. Is this the case? > > And, then, of course, there is the subject, "Agilent CGH data"-- SORRY! > > In this case, you do not want to rely on loess or other non-linear > normalization methods. Also, the MA plots for the best arrays DO show a > positive slope--this is totally expected and sought after. In other > words, with higher M-values, we expect higher A-values. > > We have found that a pretty good measure of quality of CGH arrays is the > dlrs: > > dlrs <- > function(x) { > nx <- length(x) > if (nx<3) { > stop("Vector length>2 needed for computation") > } > tmp <- embed(x,2) > diffs <- tmp[,2]-tmp[,1] > dlrs <- IQR(diffs)/(sqrt(2)*1.34) > return(dlrs) > } > > Run this on the Log ratios (ordered by chromosome and position). Good > values are less than 0.2 or so, but even some slightly higher can be used. > > As for analysis, you may want to look into the snapCGH package, as it > allows multiple analyses to be run with the same data structures. > > Sean > Hi Sean, I'd been using the loess method with the marray package, I was going by a paper i'd read regarding Agilent feature extraction software vs other pre processing methods (Zahurak et al 2007 I think). The MA plot for the raw intensities does show a positive slope, in this case which normalization method should I use? I ran dlrs and got the following: > qual <- dlrs(CNA.object[,3]) > qual [1] 0.5586258 > qual2 <- dlrs(CNA.object[,4]) > qual2 [1] 0.5778217 > qual3 <- dlrs(CNA.object[,5]) > qual3 [1] 0.5625572 As you can see I used the CNA.object for which you kindly provided a function to separate the ch from location and order them. Having done that I realized that the log to ratios I'd used are from the mnorm object (mnorm at maM), I went back and created a second CNA.object as follows: > CNA.object2 <- CNA(logratio,agilentInfo$chromosome,agilentInfo$location,data.type="lo gratio") and then got the following: > qual <- dlrs(CNA.object2[,3]) > qual [1] 0.5802947 > qual2 <- dlrs(CNA.object2[,4]) > qual2 [1] 0.6258332 > qual3 <- dlrs(CNA.object2[,5]) > qual3 [1] 0.5925305 Does this mean the quality of the arrays is poor? How would I go about referencing this (for my dissertation)? Thanks again John
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Quoting jhs1jjm at leeds.ac.uk on Tue 25 Sep 2007 20:55:53 BST: > Quoting Sean Davis <sdavis2 at="" mail.nih.gov=""> on Tue 25 Sep 2007 17:50:10 BST: > > > Sean Davis wrote: > > > jhs1jjm at leeds.ac.uk wrote: > > >> R 2.5.0 on openSUSE 10.2 x86_64. > > >> > > >> Hi, > > >> > > >> I'm using the arrayQuality package to analyse 3 44k Agilent CGH arrays > > with the > > >> aim of identifying regions of gain/loss. > > >> > > >> With the HTML report generated using the agQuality function i'm not > > getting the > > >> coloured loess curve on the MA plot for raw M. Additionally i'm only > > getting 1 > > >> value for the dot plot of controls normalized M values (-)3xLv1 (n=330) > > and > > >> likewise for the control A values. Alternatively when I run the > > maQualityPlots > > >> function on my mraw object created in marray I get these but don't get > > the > > >> comparative box plot. > > >> > > >> Firstly is this important as I'm unsure of how useful the comparative > > boxplots > > >> are as some values are NA? Secondly is this an appropriate tool to use > and > > are > > >> there any others that may be of more use both for quality control and > for > > >> analysis further down the line? Thankyou kindly for any input. > > > > > > Hi, John. Are these CGH arrays or expression arrays? The two probably > > > need some different treatment. You imply you are using CGH arrays in > > > looking for regions of gain/loss. Is this the case? > > > > And, then, of course, there is the subject, "Agilent CGH data"-- SORRY! > > > > In this case, you do not want to rely on loess or other non-linear > > normalization methods. Also, the MA plots for the best arrays DO show a > > positive slope--this is totally expected and sought after. In other > > words, with higher M-values, we expect higher A-values. > > > > We have found that a pretty good measure of quality of CGH arrays is the > > dlrs: > > > > dlrs <- > > function(x) { > > nx <- length(x) > > if (nx<3) { > > stop("Vector length>2 needed for computation") > > } > > tmp <- embed(x,2) > > diffs <- tmp[,2]-tmp[,1] > > dlrs <- IQR(diffs)/(sqrt(2)*1.34) > > return(dlrs) > > } > > > > Run this on the Log ratios (ordered by chromosome and position). Good > > values are less than 0.2 or so, but even some slightly higher can be used. > > > > As for analysis, you may want to look into the snapCGH package, as it > > allows multiple analyses to be run with the same data structures. > > > > Sean > > > Hi Sean, > > I'd been using the loess method with the marray package, I was going by a > paper > i'd read regarding Agilent feature extraction software vs other pre > processing > methods (Zahurak et al 2007 I think). The MA plot for the raw intensities > does > show a positive slope, in this case which normalization method should I use? > > I ran dlrs and got the following: > > > qual <- dlrs(CNA.object[,3]) > > qual > [1] 0.5586258 > > qual2 <- dlrs(CNA.object[,4]) > > qual2 > [1] 0.5778217 > > qual3 <- dlrs(CNA.object[,5]) > > qual3 > [1] 0.5625572 > > As you can see I used the CNA.object for which you kindly provided a function > to > separate the ch from location and order them. Having done that I realized > that > the log to ratios I'd used are from the mnorm object (mnorm at maM), I went back > and created a second CNA.object as follows: > > > CNA.object2 <- > CNA(logratio,agilentInfo$chromosome,agilentInfo$location,data.type=" logratio") > > and then got the following: > > > qual <- dlrs(CNA.object2[,3]) > > qual > [1] 0.5802947 > > qual2 <- dlrs(CNA.object2[,4]) > > qual2 > [1] 0.6258332 > > qual3 <- dlrs(CNA.object2[,5]) > > qual3 > [1] 0.5925305 > > Does this mean the quality of the arrays is poor? How would I go about > referencing this (for my dissertation)? > > Thanks again > > John > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > Sean, Don't worry about the normalization question. I've seen that this is dealt with in the snapCGH vignette so will look at that. Regards John
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